A Living Review of Machine Learning for Particle Physics

Published in

arXiv.org(2021)

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TL;DR

This living review is a nearly comprehensive list of citations for those developing and applying deep learning approaches to experimental, phenomenological, or theoretical analyses, and will be updated as often as possible to incorporate the latest developments.

Abstract

Modern machine learning techniques, including deep learning, are rapidly being applied, adapted, and developed for high energy physics. Given the fast pace of this research, we have created a living review with the goal of providing a nearly comprehensive list of citations for those developing and applying these approaches to experimental, phenomenological, or theoretical analyses. As a living document, it will be updated as often as possible to incorporate the latest developments. A list of proper (unchanging) reviews can be found within. Papers are grouped into a small set of topics to be as useful as possible. Suggestions and contributions are most welcome, and we provide instructions for participating.

Authors

M. Feickert

1 papers

B. Nachman

6 papers

References375 items

Generative Adversarial Networks

Development of convolutional neural networks for an electron-tracking Compton camera

A Reconfigurable Neural Network ASIC for Detector Front-End Data Compression at the HL-LHC

The Tracking Machine Learning Challenge: Throughput Phase

Unveiling the pole structure of S-matrix using deep learning

A Living Review of Machine Learning for Particle Physics

Published in

arXiv.org(2021)

External Links:

Generate Graph

TL;DR

Abstract

Authors

M. Feickert

1 papers

B. Nachman

6 papers

References375 items

Generative Adversarial Networks

Development of convolutional neural networks for an electron-tracking Compton camera

A Reconfigurable Neural Network ASIC for Detector Front-End Data Compression at the HL-LHC

The Tracking Machine Learning Challenge: Throughput Phase

Unveiling the pole structure of S-matrix using deep learning

CNNs for enhanced background discrimination in DSNB searches in large-scale water-Gd detectors

Invariant polynomials and machine learning

Via Machinae: Searching for Stellar Streams using Unsupervised Machine Learning

End-to-End Jet Classification of Boosted Top Quarks with the CMS Open Data

Autoencoders for unsupervised anomaly detection in high energy physics

Higgs analysis with quantum classifiers

Towards an Interpretable Data-driven Trigger System for High-throughput Physics Facilities

Understanding Event-Generation Networks via Uncertainties

Compressing PDF sets using generative adversarial networks

Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physics

Comparing weak- and unsupervised methods for resonant anomaly detection

Segmentation of EM showers for neutrino experiments with deep graph neural networks

Shower Identification in Calorimeter using Deep Learning

Charged Particle Tracking via Edge-Classifying Interaction Networks

Jet characterization in Heavy Ion Collisions by QCD-Aware Graph Neural Networks

Generalization capabilities of translationally equivariant neural networks

Elvet - a neural network-based differential equation and variational problem solver

Validation of Deep Convolutional Generative Adversarial Networks for High Energy Physics Calorimeter Simulations

Mixture density network estimation of continuous variable maximum likelihood using discrete training samples

Sparse deconvolution methods for online energy estimation in calorimeters operating in high luminosity conditions

Quantum Support Vector Machines for Continuum Suppression in B Meson Decays

Reduced Precision Strategies for Deep Learning: A High Energy Physics Generative Adversarial Network Use Case

A deep-learning based raw waveform region-of-interest finder for the liquid argon time projection chamber

Novel null tests for the spatial curvature and homogeneity of the Universe and their machine learning reconstructions

Instance Segmentation GNNs for One-Shot Conformal Tracking at the LHC

Efficient sampling of constrained high-dimensional theoretical spaces with machine learning

Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers

Bayesian Nonparametric Inference of the Neutron Star Equation of State via a Neural Network

Progress in developing a hybrid deep learning algorithm for identifying and locating primary vertices

Unsupervised event classification with graphs on classical and photonic quantum computers

Deep Learning Strategies for ProtoDUNE Raw Data Denoising

Estimation of impact parameter and transverse spherocity in heavy-ion collisions at the LHC energies using machine learning

Towards a realistic track reconstruction algorithm based on graph neural networks for the HL-LHC

Decoding Photons: Physics in the Latent Space of a BIB-AE Generative Network

A data-driven event generator for Hadron Colliders using Wasserstein Generative Adversarial Network

Ps and Qs: Quantization-Aware Pruning for Efficient Low Latency Neural Network Inference

A neural network classifier for electron identification on the DAMPE experiment

Sequence-based Machine Learning Models in Jet Physics

Point cloud transformers applied to collider physics

Learning to isolate muons

Pulling the Higgs and top needles from the jet stack with feature extended supervised tagging

Scalable, End-to-End, Deep-Learning-Based Data Reconstruction Chain for Particle Imaging Detectors

Combine and conquer: event reconstruction with Bayesian Ensemble Neural Networks

Tackling the muon identification in water Cherenkov detectors problem for the future Southern Wide-field Gamma-ray Observatory by means of machine learning

Development of a Vertex Finding Algorithm using Recurrent Neural Network

A Convolutional Neural Network based Cascade Reconstruction for the IceCube Neutrino Observatory

A convolutional-neural-network estimator of CMB constraints on dark matter energy injection

Muon identification in a compact single-layered water Cherenkov detector and gamma/hadron discrimination using machine learning techniques

MLPF: efficient machine-learned particle-flow reconstruction using graph neural networks

The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics

Learning from many collider events at once

Hashing and metric learning for charged particle tracking

A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications

Deep-Learning based Reconstruction of the Shower Maximum $X_{\mathrm{max}}$ using the Water-Cherenkov Detectors of the Pierre Auger Observatory

Lattice gauge equivariant convolutional neural networks

How to GAN Higher Jet Resolution

Application of quantum machine learning using the quantum variational classifier method to high energy physics analysis at the LHC on IBM quantum computer simulator and hardware with 10 qubits

Unsupervised in-distribution anomaly detection of new physics through conditional density estimation

Image-Based Jet Analysis

Jet tagging in the Lund plane with graph networks

Uncovering hidden patterns in collider events with Bayesian probabilistic models

Particle Track Reconstruction using Geometric Deep Learning

Deep learning jet modifications in heavy-ion collisions

Semantic segmentation with a sparse convolutional neural network for event reconstruction in MicroBooNE

How to GAN Event Unweighting

Explainable machine learning of the underlying physics of high-energy particle collisions

Deep-Learning-Based Kinematic Reconstruction for DUNE

Deep learning analysis of deeply virtual exclusive photoproduction

Simulating the time projection chamber responses at the MPD detector using generative adversarial networks

Distributed Training and Optimization Of Neural Networks

Equivariant energy flow networks for jet tagging

Inclusive search for highly boosted Higgs bosons decaying to bottom quark-antiquark pairs in proton-proton collisions at s\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek}

Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics

Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs

Search for Higgs Boson Decays into a Z Boson and a Light Hadronically Decaying Resonance Using 13 TeV pp Collision Data from the ATLAS Detector.

Phase space sampling and inference from weighted events with autoregressive flows

Explainable AI for ML jet taggers using expert variables and layerwise relevance propagation

Inverting cosmic ray propagation by convolutional neural networks

Maximum performance of strange-jet tagging at hadron colliders

Machine learning approach for the search of resonances with topological features at the Large Hadron Collider

Improving sensitivity to low-mass dark matter in LUX using a novel electrode background mitigation technique

Study of Using Machine Learning for Level 1 Trigger Decision in JUNO Experiment

Hierarchical clustering in particle physics through reinforcement learning

Real-time artificial intelligence for accelerator control: A study at the Fermilab Booster

Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference

Evolutionary algorithms for hyperparameter optimization in machine learning for application in high energy physics

First application of machine learning algorithms to the position reconstruction in Resistive Silicon Detectors

Learning to identify electrons

Anomaly Detection for Physics Analysis and Less Than Supervised Learning

Morphology for jet classification

Mapping machine-learned physics into a human-readable space

Boosted Top Quark Tagging and Polarization 2 Measurement using Machine Learning

Enhancing searches for resonances with machine learning and moment decomposition

Convolutional neural network for multiple particle identification in the MicroBooNE liquid argon time projection chamber

100

Extracting Signals of Higgs Boson From Background Noise Using Deep Neural Networks

101

FPGAs-as-a-Service Toolkit (FaaST)

102

Combining outlier analysis algorithms to identify new physics at the LHC

103

Targeted Likelihood-Free Inference of Dark Matter Substructure in Strongly-Lensed Galaxies

104

Simulation-Based Inference Methods for Particle Physics

105

Neural-network analysis of Parton Distribution Functions from Ioffe-time pseudodistributions

106

Parameter estimation using neural networks in the presence of detector effects

107

MCNNTUNES: Tuning Shower Monte Carlo generators with machine learning

108

Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning

109

Phenomenology of vector-like leptons with Deep Learning at the Large Hadron Collider

110

Unsupervised clustering for collider physics

111

Improved neural network Monte Carlo simulation

112

PDFFlow: parton distribution functions on GPU

113

Detecting Subhalos in Strong Gravitational Lens Images with Image Segmentation

114

Benchmarking Machine Learning Techniques with Di-Higgs Production at the LHC

115

Disentangling boosted Higgs Boson production modes with machine learning

116

Simulation-assisted decorrelation for resonant anomaly detection

117

DCTRGAN: improving the precision of generative models with reweighting

118

Variational Autoencoders for Jet Simulation

119

Graph neural network for 3D classification of ambiguities and optical crosstalk in scintillator-based neutrino detectors

120

An Attention Based Neural Network for Jet Tagging

121

Reconstructing boosted Higgs jets from event image segmentation

122

Mass Unspecific Supervised Tagging (MUST) for boosted jets

123

Decoding Dark Matter Substructure without Supervision

124

Matrix element regression with deep neural networks — Breaking the CPU barrier

125

Jet flavour classification using DeepJet

126

Studying the potential of Graphcore IPUs for applications in Particle Physics

127

Generative Networks for LHC Events

128

Sensing Higgs cascade decays through memory

129

Linearized optimal transport for collider events

130

Data-driven quark- and gluon-jet modification in heavy-ion collisions

131

Supervised jet clustering with graph neural networks for Lorentz boosted bosons

132

Comparing traditional and deep-learning techniques of kinematic reconstruction for polarization discrimination in vector boson scattering

133

Invisible Higgs search through vector boson fusion: a deep learning approach

134

'Deep' Dive into $b \to c$ Anomalies: Standardized and Future-proof Model Selection Using Self-normalizing Neural Networks

135

Data-driven Estimation of Background Distribution through Neural Autoregressive Flows

136

Deep learning analysis of the inverse seesaw in a 3-3-1 model at the LHC

137

Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics

138

AI-based Monte Carlo event generator for electron-proton scattering

139

A review on machine learning for neutrino experiments

140

Unsupervised outlier detection in heavy-ion collisions

141

Distinguishing W′ signals at hadron colliders using neural networks

142

MLaaS4HEP: Machine Learning as a Service for HEP

143

Automating the ABCD method with machine learning

144

Graph neural networks in particle physics

145

Exploring the Possibility of a Recovery of Physics Process Properties from a Neural Network Model

146

Augmented signal processing in Liquid Argon Time Projection Chambers with a deep neural network

147

Neural resampler for Monte Carlo reweighting with preserved uncertainties

148

Parametrized classifiers for optimal EFT sensitivity

149

GPU coprocessors as a service for deep learning inference in high energy physics

150

Dealing with Nuisance Parameters using Machine Learning in High Energy Physics: a Review

151

Inclusive search for highly boosted Higgs bosons decaying to bottom quark-antiquark pairs in proton-proton collisions at $\sqrt{s} =$ 13 TeV

152

Bayesian Neural Networks for Fast SUSY Predictions

153

Towards machine learning analytics for jet substructure

154

Scalable, Proposal-free Instance Segmentation Network for 3D Pixel Clustering and Particle Trajectory Reconstruction in Liquid Argon Time Projection Chambers

155

Variational autoencoders for anomalous jet tagging

156

An updated hybrid deep learning algorithm for identifying and locating primary vertices

157

Track Seeding and Labelling with Embedded-space Graph Neural Networks

158

Xsec: the cross-section evaluation code

159

Casting a graph net to catch dark showers

160

End-to-End Sinkhorn Autoencoder With Noise Generator

161

Invertible networks or partons to detector and back again

162

Finding new physics without learning about it: anomaly detection as a tool for searches at colliders

163

PILArNet: Public Dataset for Particle Imaging Liquid Argon Detectors in High Energy Physics

164

Numerical analysis of neutrino physics within a high-scale supersymmetry model via machine learning

165

Learning the latent structure of collider events

166

Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed

167

Dijet Resonance Search with Weak Supervision Using sqrt[s]=13 TeV pp Collisions in the ATLAS Detector.

168

Identifying heavy-flavor jets using vectors of locally aggregated descriptors

169

Adversarial domain adaptation to reduce sample bias of a high energy physics classifier

170

Boosted decision trees approach to neck alpha events discrimination in DEAP-3600 experiment

171

Fast convolutional neural networks for identifying long-lived particles in a high-granularity calorimeter

172

Use of a generalized energy Mover’s distance in the search for rare phenomena at colliders

173

Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques

174

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

175

Flows for simultaneous manifold learning and density estimation

176

Neural network-based top tagger with two-point energy correlations and geometry of soft emissions

177

Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors

178

Per-object systematics using deep-learned calibration

179

Strange Jet Tagging

180

Optimal Statistical Inference in the Presence of Systematic Uncertainties Using Neural Network Optimization Based on Binned Poisson Likelihoods with Nuisance Parameters

181

Equivariant flow-based sampling for lattice gauge theory

182

Compressing deep neural networks on FPGAs to binary and ternary precision with hls4ml

183

VegasFlow: Accelerating Monte Carlo simulation across multiple hardware platforms

184

Tag N’ Train: a technique to train improved classifiers on unlabeled data

185

Using neural networks for efficient evaluation of high multiplicity scattering amplitudes

186

Resonance Searches with Machine Learned Likelihood Ratios

187

Fast inference of Boosted Decision Trees in FPGAs for particle physics

188

On the impact of selected modern deep-learning techniques to the performance and celerity of classification models in an experimental high-energy physics use case

189

Event generation with normalizing flows

190

Exploring phase space with Neural Importance Sampling

191

Simulation assisted likelihood-free anomaly detection

192

Anomaly detection with density estimation

193

ABCNet: an attention-based method for particle tagging

194

DisCo Fever: Robust Networks Through Distance Correlation

195

A deep neural network to search for new long-lived particles decaying to jets

196

Machine learning amplitudes for faster event generation

197

How to GAN event subtraction

198

Machine and deep learning applications in particle physics

199

Calorimetry with deep learning: particle simulation and reconstruction for collider physics

200

A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution

201